Retrieval of soil moisture based on Gaofen-3 (GF-3) satellite synthetic aperture radar data over agricultural fields

doi:10.3785/j.issn.1008-9209.2023.12.183

Journal of Zhejiang University (Agriculture and Life Sciences)

2024, Vol. 50

Issue (2): 209-220 DOI: 10.3785/j.issn.1008-9209.2023.12.183

Retrieval of soil moisture based on Gaofen-3 (GF-3) satellite synthetic aperture radar data over agricultural fields

Linlin ZHANG^1,^2,³(

),Zhibin LEI⁴(

),Liping WANG⁵,Qingyan MENG^1,^2,³(

),Jiangyuan ZENG¹

^1.State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
^2.University of Chinese Academy of Sciences, Beijing 100049, China
^3.Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, Hainan, China
^4.School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China
^5.Center for Urban Governance Studies of Zhejiang Province, Hangzhou International Urbanology Research Center, Hangzhou 310000, Zhejiang, China

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Abstract

Soil moisture is the basic condition for crop growth. A new retrieval algorithm for soil moisture was proposed based on C-band synthetic aperture radar (SAR) data from Gaofen-3 (GF-3) satellite, and soil moisture of agricultural fields with a regional scale spatial resolution of 8 m was obtained. First, the algorithm selected the optical vegetation water index based on PROSAIL model, measured vegetation canopy water content and Landsat-8 optical data. The parameters of water cloud model were calculated， and soil direct backscattering coefficients were obtained. Second, the radar backscattering influence mechanism was simulated using an advanced integral equation model (AIEM), and the combined roughness of soil surface was calculated based on the characteristics of radar data at high and low incidence angles. Finally, soil moisture was retrieved using co-polarization radar data from GF-3 satellite over agricultural fields, and this was verified with measured data. The results showed that there was a high consistency between the measured soil moisture and estimated soil moisture, and vertical-vertical (VV) polarization exhibited higher retrieval accuracy, with a determination coefficient of 0.595 6 and a root mean square error of 0.041 5 m³/m³. The results can provide algorithmic references for the GF-3 satellite to obtain high-resolution soil moisture information.

Key words： soil moisture Gaofen-3 (GF-3) satellite radar remote sensing soil surface roughness

Received: 18 December 2023 Published: 25 April 2024

CLC:

P237

Corresponding Authors: Qingyan MENG E-mail: zhangll@aircas.ac.cn;1529418402@qq.com;mengqy@radi.ac.cn

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Cite this article:

Linlin ZHANG, Zhibin LEI, Liping WANG, Qingyan MENG, Jiangyuan ZENG. Retrieval of soil moisture based on Gaofen-3 (GF-3) satellite synthetic aperture radar data over agricultural fields. Journal of Zhejiang University (Agriculture and Life Sciences), 2024, 50(2): 209-220.

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https://www.zjujournals.com/agr/10.3785/j.issn.1008-9209.2023.12.183 OR https://www.zjujournals.com/agr/Y2024/V50/I2/209

基于高分三号卫星合成孔径雷达数据的农田土壤水分反演

土壤水分是农作物生长的基本条件，本研究基于高分三号卫星C波段合成孔径雷达数据，提出新的土壤水分反演算法，并获取区域尺度8 m空间分辨率的农田区土壤水分。首先，通过PROSAIL模型、实测植被冠层含水量、Landsat-8光学数据优选光学植被水分指数，计算水云模型参数并获得土壤直接后向散射系数；其次，利用高级积分方程模型模拟雷达后向散射影响机制，采用雷达影像高低入射角特性计算地表组合粗糙度；最后，利用高分三号卫星同极化雷达数据反演农田区土壤水分，并基于实测数据开展精度验证。结果表明：土壤水分反演值与野外实测值具有良好一致性，垂直极化下反演精度更高，其决定系数为0.595 6，均方根误差为0.041 5 m³/m³。本研究成果可为我国自主研发的高分三号卫星获取高分辨率土壤水分信息提供算法参考。

关键词： 土壤水分, 高分三号卫星, 雷达遥感, 地表粗糙度

Fig. 1 Backscattering coefficient of GF-3 satellite radar in study areaThe black dots represent sampling areas in the field experiment, and the legend represents the backscattering coefficient, dB.

植被水分指数 Vegetation water index	公式 Formula	文献 Reference
简单比值指数 Simple ratio index (SR)	$S R = R N i r R R e d$	[22]
水胁迫指数 Moisture stress index (MSI)	$M S I = R S w i r 1 R N i r$	[23]
归一化差异水指数1640 Normalized difference water index 1640 (NDWI₁₆₄₀)	$N D W I 1640 = R N i r - R S w i r 1 R N i r + R S w i r 1$	[24]
归一化差异水指数2201 NDWI₂₂₀₁	$N D W I 2201 = R N i r - R S w i r 2 R N i r + R S w i r 2$	[24]
归一化植被指数 Normalized difference vegetation index (NDVI)	$N D V I = R N i r - R R e d R N i r + R R e d$	[25]
归一化多波段干旱指数 Normalized multi-band drought index (NMDI)	$N M D I = R N i r - (R S w i r 1 - R S w i r 2) R N i r + (R S w i r 1 + R S w i r 2)$	[26]
四波段干旱指数 Four band combined drought index (FCDI)	$F C D I = R S w i r 1 / R S w i r 2 (R N i r - R G r e e n) / (R N i r + R G r e e n)$	[27]
增强型植被指数 Enhanced vegetation index (EVI)	$E V I = 2.5 R N i r - R R e d R N i r + 6 R S w i r 1 - 7.5 R B l u e + 1$	[28]

Table 1 Computational formulas of eight vegetation water indexes

Table 2 Different parameters of PROSAIL model

Fig. 2 Correlation between vegetation water index and simulated vegetation canopy water content (VCWC)

Table 3 Correlation between vegetation water index and measured vegetation canopy water content in the field

Fig. 3 Soil direct backscattering coefficient imageA. Retrieved value from GF-3 satellite radar; B. Scatter plots of retrieved value from GF-3 satellite radar and simulated value from AIEM.

Fig. 4 Response of incidence angle to soil direct backscattering coefficient in different soil moisture conditions

Fig. 6 Response of combined roughness to soil direct back-scattering coefficient

Fig. 7 Three-dimensional relationship among soil direct backscattering coefficient, soil moisture and combined roughness

Fig. 8 Response of combined roughness to differences of soil direct backscattering coefficients at high and low incidence angles

Fig. 9 Soil moisture map retrieved by the GF-3 satellite radarPink patches represent villages.

Fig. 10 Scatter plots of retrieved and measured values of soil moisture


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